Abstract. In this article, we review some recent advances in testing for serial
correlation, provide code for implementation, and illustrate this code's
application to market risk forecast evaluation. We focus on the classic and
widely used portmanteau tests and their data-driven versions. These tests are
simple to implement for two reasons: First, the researcher does not need to
specify the order of the tested autocorrelations, because the test
automatically chooses this number. Second, its asymptotic null distribution is
chi-squared with one degree of freedom, so there is no need to use a bootstrap
procedure to estimate the critical values. We illustrate the wide applicability
of this methodology with applications to forecast evaluation for market risk
measures such as value-at-risk and expected shortfall.